A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms

The mesencephalic locomotor region recruits V2a reticulospinal neurons to drive forward locomotion in larval zebrafish

The mesencephalic locomotor region (MLR) is a brain stem area whose stimulation triggers graded forward locomotion. How MLR neurons recruit downstream vsx2+ (V2a) reticulospinal neurons (RSNs) is poorly understood. Here, to overcome this challenge, we uncovered the locus of MLR in transparent larval zebrafish and show that the MLR locus is distinct from the nucleus of the medial longitudinal fasciculus. MLR stimulations reliably elicit forward locomotion of controlled duration and frequency. MLR neurons recruit V2a RSNs via projections onto somata in pontine and retropontine areas, and onto dendrites in the medulla. High-speed volumetric imaging of neuronal activity reveals that strongly MLR-coupled RSNs are active for steering or forward swimming, whereas weakly MLR-coupled medullary RSNs encode the duration and frequency of the forward component. Our study demonstrates how MLR neurons recruit specific V2a RSNs to control the kinematics of forward locomotion and suggests conservation of the motor functions of V2a RSNs across vertebrates. Carbo-Tano and colleagues investigate the mesencephalic locomotor region in larval zebrafish and its role in triggering forward locomotion by activating specific sets of hindbrain V2a reticulospinal neurons

An Image-Free Opto-Mechanical System for Creating Virtual Environments and Imaging Neuronal Activity in Freely Moving Caenorhabditis elegans

Non-invasive recording in untethered animals is arguably the ultimate step in the analysis of neuronal function, but such recordings remain elusive. To address this problem, we devised a system that tracks neuron-sized fluorescent targets in real time. The system can be used to create virtual environments by optogenetic activation of sensory neurons, or to image activity in identified neurons at high magnification. By recording activity in neurons of freely moving C. elegans, we tested the long-standing hypothesis that forward and reverse locomotion are generated by distinct neuronal circuits. Surprisingly, we found motor neurons that are active during both types of locomotion, suggesting a new model of locomotion control in C. elegans. These results emphasize the importance of recording neuronal activity in freely moving animals and significantly expand the potential of imaging techniques by providing a mean to stabilize fluorescent targets

Spatiotemporal visual statistics of aquatic environments in the natural habitats of zebrafish

Abstract Animal sensory systems are tightly adapted to the demands of their environment. In the visual domain, research has shown that many species have circuits and systems that exploit statistical regularities in natural visual signals. The zebrafish is a popular model animal in visual neuroscience, but relatively little quantitative data is available about the visual properties of the aquatic habitats where zebrafish reside, as compared to terrestrial environments. Improving our understanding of the visual demands of the aquatic habitats of zebrafish can enhance the insights about sensory neuroscience yielded by this model system. We analyzed a video dataset of zebrafish habitats captured by a stationary camera and compared this dataset to videos of terrestrial scenes in the same geographic area. Our analysis of the spatiotemporal structure in these videos suggests that zebrafish habitats are characterized by low visual contrast and strong motion when compared to terrestrial environments. Similar to terrestrial environments, zebrafish habitats tended to be dominated by dark contrasts, particularly in the lower visual field. We discuss how these properties of the visual environment can inform the study of zebrafish visual behavior and neural processing and, by extension, can inform our understanding of the vertebrate brain

Data from: A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms

Optic flow in the natural habitats of zebrafish supports spatial biases in visual self-motion estimation

Animals benefit from knowing if and how they are moving. Across the animal kingdom, sensory information in the form of optic flow over the visual field is used to estimate self-motion. However, different species exhibit strong spatial biases in how they use optic flow. Here, we show computationally that noisy natural environments favor visual systems that extract spatially biased samples of optic flow when estimating self-motion. The performance associated with these biases, however, depends on interactions between the environment and the animal's brain and behavior. Using the larval zebrafish as a model, we recorded natural optic flow associated with swimming trajectories in the animal's habitat with an omnidirectional camera mounted on a mechanical arm. An analysis of these flow fields suggests that lateral regions of the lower visual field are most informative about swimming speed. This pattern is consistent with the recent findings that zebrafish optomotor responses are preferentially driven by optic flow in the lateral lower visual field, which we extend with behavioral results from a high-resolution spherical arena. Spatial biases in optic-flow sampling are likely pervasive because they are an effective strategy for determining self-motion in noisy natural environments

wormtracks data archive

All raw data, plus the main analysis program and spreadsheet